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Vol.34 No.4
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Deep Learning Research Trends Analysis with Ego Centered Topic Citation Analysis

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2017, v.34 no.4, pp.7-32
https://doi.org/10.3743/KOSIM.2017.34.4.007

(2017). Deep Learning Research Trends Analysis with Ego Centered Topic Citation Analysis. Journal of the Korean Society for Information Management, 34(4), 7-32, https://doi.org/10.3743/KOSIM.2017.34.4.007
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Abstract

Recently, deep learning has been rapidly spreading as an innovative machine learning technique in various domains. This study explored the research trends of deep learning via modified ego centered topic citation analysis. To do that, a few seed documents were selected from among the retrieved documents with the keyword ‘deep learning’ from Web of Science, and the related documents were obtained through citation relations. Those papers citing seed documents were set as ego documents reflecting current research in the field of deep learning. Preliminary studies cited frequently in the ego documents were set as the citation identity documents that represents the specific themes in the field of deep learning. For ego documents which are the result of current research activities, some quantitative analysis methods including co-authorship network analysis were performed to identify major countries and research institutes. For the citation identity documents, co-citation analysis was conducted, and key literatures and key research themes were identified by investigating the citation image keywords, which are major keywords those citing the citation identity document clusters. Finally, we proposed and measured the citation growth index which reflects the growth trend of the citation influence on a specific topic, and showed the changes in the leading research themes in the field of deep learning.

keywords
딥러닝, 기계학습, 연구동향 분석, 자아중심 주제인용분석, 공저 네트워크, 동시인용분석, 인용 이미지 키워드, 인용 성장지수, deep learning, machine learning, research trends analysis, ego centered topic citation analysis, coauthorship networks, co-citation analysis, citation image keywords, citation growth index

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